Abstract
This paper presents a DTCNN model for dynamic and static object segmentation in videos. The proposed method involves three main stages in the dynamic stage; dynamic background registration, dynamic objects detection and object segmentation improvement. Two DTCNNs are used, one to achieved object detection and other for morphologic operations in order to improve object segmentation. The static segmentation stage is composed of a clustering module and a DTCNN module. The clustering module is in charge of detecting the possible regions and the DTCNN generate the regions. Visual and quantitative results indicate acceptable results compared with existing methods.
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The authors thanks to DGEST, by the support of this research under grant CHI-MCIET-2012-105.
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Chacon-Murguia, M.I., Urias-Zavala, D. (2014). A DTCNN Approach on Video Analysis: Dynamic and Static Object Segmentation. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_22
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DOI: https://doi.org/10.1007/978-3-319-05170-3_22
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